A Physical Model for Microstructural Characterization and Segmentation of 3D Tomography Data

We present a novel method for characterizing the microstructure of a material from volumetric datasets such as 3D image data from computed tomography (CT). The method is based on a new statistical model for the distribution of voxel intensities and gradient magnitudes, incorporating prior knowledge about the physical nature of the imaging process. It allows for direct quantification of parameters of the imaged sample like volume fractions, interface areas and material density, and parameters related to the imaging process like image resolution and noise levels. Existing methods for characterization from 3D images often require segmentation of the data, a procedure where each voxel is labeled according to the best guess of which material it represents. Through our approach, the segmentation step is circumvented so that errors and computational costs related to this part of the image processing pipeline are avoided. Instead, the material parameters are quantified through their known relation to parameters of our model which is fitted directly to the raw, unsegmented data. We present an automated model fitting procedure that gives reproducible results without human bias and enables automatic analysis of large sets of tomograms. For more complex structure analysis questions, a segmentation is still beneficial. We show that our model can be used as input to existing probabilistic methods, providing a segmentation that is based on the physics of the imaged sample. Because our model accounts for mixed-material voxels stemming from blurring inherent to the imaging technique, we reduce the errors that other methods can create at interfaces between materials.Comment: Manuscript accepted for publication in Materials Characterizatio

Formation of desert rose structures in vacuum plasma sprayed electrodes for alkaline electrolysis

The EU FCH-JU RESelyser project is concerned with the development of high pressure, high efficiency and low cost alkaline water electrolysers that can be operated variably and intermittently to meet the demands for integration into energy networks relying on fluctuating renewable energy. The project utilizes NiAlMo alloy electrodes produced at the German Aerospace Center (DLR) by vacuum plasma spraying (VPS). VPS results in a heterogeneous microstructure consisting of a multitude of intermetallic phase sub domains and pores. Prior to electrolysis operation the electrodes are activated by leaching of Al and some Al containing intermetallic phases leaving micrometer pores and nanometer dendritic pores increasing the surface area available for the electrolysis reactions. The vacuum plasma sprayed electrodes were analyzed by high resolution SEM and TEM before and after electrolysis operation and after storage in water. Analyses of cross sections and electrode surfaces revealed desert rose like nano flake structures on the surface and in the pores on several electrodes. The formation of the desert rose structure appeared to be related to the electrolysis operation as well as the duration of storage in distilled water. The size of the faceted flakes varied from tens of nm to a couple of µm where the thickness varied from a few nm to ~50 nm. The desert rose structure was confirmed by TEM to consist primarily of NiO and Al2NiO4 like phases (similar lattice parameters). The possible implications for the application and performance of the electrodes are discussed